Advancing Patient Safety and Care Quality via AI Integration in Nursing, Surgery, EMS, Anesthesia, Administration, Coding, and Psychology in University Hospitals
Patient safety in university hospitals remains a critical concern due to complex medical procedures, high patient acuity, and the persistent risk of hospital-acquired infections (HAIs). This narrative review examines literature from 2020 to 2024 to explore how artificial intelligence (AI) enhances collaboration among nursing, operations, anesthesia, medical coding, paramedics, and healthcare management to improve patient safety and reduce infection rates in university hospital settings. By synthesizing existing studies, it highlights each discipline’s unique contributions, the synergies enabled by AI, and the barriers to its adoption. The findings reveal that AI improves real-time monitoring, resource management, risk prediction, documentation accuracy, emergency triage, and strategic planning, fostering cohesive teamwork and reducing HAIs by 15- 20%. However, challenges such as inadequate training, system interoperability issues, and ethical concerns persist. Recommendations include tailored training programs, standardized systems, and robust ethical frameworks. Future directions suggest developing integrated AI platforms and conducting long-term outcome studies. This review underscores AI’s transformative potential to unite interdisciplinary teams, enhancing patient safety and infection control in academic medical environments.